Helping people use AI well, not just talk about it.

Early image classifiers don't "see" a dog or a pastry — they match pixel patterns like dark round eyes and blobby brown shapes. A muffin studded with chocolate chips is, pattern-wise, uncomfortably close to a chihuahua's face. It became a famous meme for a real limitation: pattern-matching isn't the same as understanding.

It was coined in 1956 by computer scientist John McCarthy, for a summer workshop at Dartmouth College where a small group of researchers proposed that "every aspect of learning" could in principle be simulated by a machine. That workshop is generally treated as the founding moment of AI as a field.

It's when a model states something false with total confidence — a fake statistic, a made-up source, a book that doesn't exist. It happens because language models are built to predict plausible-sounding text, not to check facts against reality. Fluent and correct are two different things, and it's worth verifying anything that matters.

Never completely. Speech recognition ranks a list of possible sentences by probability and picks the most likely one. That's why background noise, accents, or mumbling can turn "call my mom" into something else entirely — it wasn't confused, it just picked wrong from a list of guesses.

Google's DeepDream project ran an image classifier backwards: instead of identifying patterns, it exaggerated whatever patterns it thought it already saw, layering eyes and animal shapes into ordinary photos. It looked psychedelic, but it was really just a debugging tool for seeing what a network had learned, turned into art.

They're trained on huge amounts of human writing, and confident, decisive writing is far more common online than hedged, uncertain writing. The model learns that tone as the "normal" way to answer — which is exactly why it's worth double-checking anything specific, like dates, numbers, or citations.